Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Justin M Luningham, Daniel B McArtor, Anne M Hendriks, Catharina E M van Beijsterveldt, Paul Lichtenstein, Sebastian Lundström, Henrik Larsson, Meike Bartels, Dorret I Boomsma, Gitta H Lubke

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.

Original languageEnglish
Article number1227
Pages (from-to)1-16
Number of pages16
JournalFrontiers in Genetics
Volume10
DOIs
Publication statusPublished - 10 Dec 2019

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Bibliographical note

Copyright © 2019 Luningham, McArtor, Hendriks, van Beijsterveldt, Lichtenstein, Lundström, Larsson, Bartels, Boomsma and Lubke.

Keywords

  • consortia
  • data integration
  • genome-wide association studies
  • latent variable modeling
  • phenotype harmonization

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